For comparison, we report experiments employing Trimap [1]}, [2]}, which focuses on boundary regions of segmentation; see evaluation region in Fig REF .
The Trimap is a rough image segmentation in the foreground, background, and unknown regions, shown in Fig. REF  with white, black, and gray regions, respectively.
The idea is to define a narrow band (gray region defined using a width pixel) around each contour and compute pixel-wise accuracy in the given band.
The error curve comparison (plots in Fig. REF ) shows that learning the contour-based auxiliary tasks did not allow the network to overfit, enabling the networks to generalize better.
<FIGURE>